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I am looking for a new PhD candidate to work with me on tree-insect networks in the IDENT network of tree diversity experiments. I am looking forward to your applications! See more info here: PhD-position_IDENT_JFruend2017

I have a PhD position (65% contract for 3 years) available in my lab (at the University of Freiburg)!

You will use models and fieldwork to investigate temporal dynamics in insect interaction networks (plant-pollinator, plant-herbivore, host-parasitoid). The main objective is to identify the functional relevance of temporal (daily, seasonal) structure of species interactions. The degree of modeling vs empirical work is somewhat flexible.

With a little delay caused by my moving back to Germany, I’d like to to advertise my most recent paper (available in Oikos, includes a rich online appendix), which I find really important:

The network approach is currently popular in ecology, as it promises to manage the complexity of interactions among many different species. When network studies try to include all interacting species, some of them will inevitably be represented by few observations. Specialization, a major aspect of network structure, is overestimated with few observations: there is a sampling bias (in the sense of “bias due to sampling”). Trying to understand the functional relevance of biodiversity and the impact of environmental change on communities and ecosystems, more studies now compare different networks, which further limits how much effort can be spent to sample each network.

In this study, we developed a model that generates realistic quantitative interaction networks and used it to evaluate methods that try to overcome sampling bias in specialization estimates. We found that, unfortunately, all metrics and methods currently used for network analysis misrepresent true network structure when used on data with realistic numbers of observations. Although some metrics performed reasonably well, caution should be used when comparing empirical network patterns to theoretical predictions and when comparing different networks. Our model could be useful for carefully evaluating the potential for sampling bias for a given study and develop new methods to correct quantitative estimates of network structure. Our study also highlights the large potential for sampling bias in studies estimating specialization without a network focus.

Together with Gita Benadi, I was invited to write a guest post on Florian Hartig’s blog theoreticalecology. We basically argue why a positive effect of nestedness on stability of mutualistic networks should not be considered an established fact, as long as it is not confirmed by dynamic models that take into account that mutualistic services are resources for which species may compete, in difference to current models, in which mutualistic services effectively reduce competition.

This post calls one of the most-cited ‘laws’ for plant-pollinator networks back into the arena, so check it out!

What happens to wild bees if the winter gets warmer? We performed an experiment looking at temperature effects on different bee species during a time when nobody thinks about them, because they don’t fly around. The paper, recently published online in Oecologia, shows that bee diversity might be important as an insurance against climate change: different bee species respond differently to higher overwintering temperatures. For some species, warmer winter means higher metabolism (and thus increased weight loss and early emergence), but not for others. As common in ecology, the consequences are not too simple to predict: some of this ‘response diversity’ is related to the time of year a species is normally active. Read more…

I have recently started my new postdoc position with Kevin McCann at the University of Guelph. In our collaboration at the Canadian Forest Service, Fredericton, I am working with Eldon Eveleigh on an amazing dataset of host-parasitoid interactions from forests in New Brunswick. After my previous focus on mutualistic and competitive interactions, I feel that it is time to learn something about antagonistic interactions (nasty consumers that kill their resources).